Fault Diagnosis of Wind Turbine Gearbox Based on Deep Bi-Directional Long Short-Term Memory Under Time-Varying Non-Stationary Operating Conditions

Fault Diagnosis of Wind Turbine Gearbox Based on Deep Bi-Directional Long Short-Term Memory Under Time-Varying Non-Stationary Operating Conditions

Fault diagnosis of wind turbine (WT) gearboxes can reduce unexpected downtime and maintenance costs. In this paper, a new fault diagnosis framework is proposed based on deep bi-directional Long Short-term Memory (DB-LSTM). Even though deep learning has been used in fault diagnosis of rotating machin...

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Bibliographic Details
Main Authors: Lixiao Cao, Zheng Qian, Hamidreza Zareipour, Zhenkai Huang, Fanghong Zhang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Fault diagnosis
wind turbine gearbox
bi-directional long short-term memory
time varying non-stationary operating condition
Online Access:https://ieeexplore.ieee.org/document/8869884/

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